APICS SEMINA=
R 1 May
2004
SIMULATION M=
ODELING
IN LEAN PROGRAMS
Jim Curry,
CEO OpStat Group, www.OpStat.com
<=
/o:p>
<=
/o:p>
Simulation modeling is a valuable tool that has=
been
used for over 30 years to analyze systems with statistical randomness in i=
ts
operations and transaction flows.
The advances made in simulation tools since the mid-1990’s ha=
ve
made them an essential part of a lean program to improve manufacturing and
supply chain operations.
Simulation is most valuable in complex processes
where there are parameters and transactions with uncertainty and variabili=
ty in
demand, supply, and operational processes.
It has been used successfully in factory floor improvement, invento=
ry
management, product mix analysis, process design and supply chain improvem=
ent
with techniques such as postponement.
Simulation modeling provides the next step in
improvement analysis to traditional lean improvement tools such as process
mapping. Simulation models a=
dd the
dynamic representations that show bottlenecks in current processes, as wel=
l as
effects of projected changes in processes.
DEFINITION OF SIMULATION
Simulation is a set of mathematical and logical
relationships that describe the operation of real-world complex systems, w=
here
one or more of the relationships are probabilistic, also defined as monte =
carlo
simulation.
Simulation models depict results over a period =
of
time to show how an operation performs during peaks as well as lower volume
time periods. It is used in =
a trial
and error manner using sensitivity analysis to determine causes and effect=
s, as
well as solutions.
Most manufacturing operations involve complexit=
ies
that make it impossible to define an equation, or deterministic, approach =
to a
solution model. Simulation m=
odels
are based on actual processing rules for operations such as changeovers, s=
etups
and cleanups, and are driven by actual patterns of demand and supply, that
combined provide a representation of actual or projected results.
TYPES OF PROBLEMS & ANALYSES
Catego=
ries of
lean programs in which simulation has been used effectively are:
· &=
nbsp;
Analysis of capacity, utilization and throughput
improvement,
· &=
nbsp;
Balancing supply and demand, inventory and service lev=
el
management, and
· &=
nbsp;
A combination of these.
CAPACITY ANALYSES
An example of one typical capacity analysis project was in a high
volume consumer goods manufacturing plant for liquid cough remedies. The manufacturing process itself
involved a series of both process and discrete activities, for approximate=
ly 95
products. The process begins=
with a
chemical mixing process, and then is followed by a set of packaging operat=
ions
to make approximately 115 million bottles per year.
Following is the master input control screen for the model that al=
lows
the analyst to adjust volumes and mix, essential parameters such as cleani=
ng
system resources, seasonality of demand, and crewing schedules. Extend, a discrete event simulati=
on
modeling tool from Imagine That Inc., is used for all models depicted in t=
his
paper.
<=
![endif]>
After a preliminary statistical analysis of the patterns of demand=
and
production, it was determined that a key driver of performance was the num=
ber of
campaigns, or batches grouped together to reduce the amount of time spent =
on
changeovers and cleanups between successive products.
The following provides an overview of the modeling approach.<=
/o:p>
<=
![endif]>
Since campaign patterns were a key driver of pe=
rformance,
the model was developed to allow a variety of statistical patterns to mirr=
or
the actual patterns. The fol=
lowing
table summarizes the actual historical patterns in approximately 1,300 bat=
ches
produced over 18 months:
|
Category
|
Number of Products
|
Number of Campaigns
|
|
Single batches
|
45
|
NA
|
|
Long & infrequent campaigns
|
6
|
40
|
|
High runners
|
6
|
140
|
|
Medium / low volume
|
30
|
80
|
|
Bulk only; no packaging
|
3
|
NA
|
|
Actual data for each significant category was=
run
through statistical fitting software to develop appropriate statistical
patterns. Where there was =
not a
fit to a standard distribution, empirical distributions were developed s=
uch
as these.
|

|
The modeling methodology included interviews wi=
th
planners and schedulers over several weeks, then model construction using =
the
rules defined through those interviews, and analysis of the actual product=
ion
patterns. =
The first step in any modeling project is valid=
ation
of the rules and statistical patterns.&nb=
sp;
A baseline with actual data is run to match the model metrics deriv=
ed to
actual performance metrics for the facility. This validates the rules defined.=
Then the model is switched to
statistical mode, and run with current levels of activities and rules to
validate the statistical patterns used.&n=
bsp;
The model is then ready for analysis with sets =
of
scenarios and sensitivity analysis to identify root causes of problems, and
predicted results of changes in rules, capacities and volumes. Following is a set of examples of
results on utilization and processing times that are produced by the model=
s.
<=
![endif]>
The model was used for hundreds of simulated runs as capacity incr=
eases
were planned and analyzed to increase performance of the facility. Since these products are regulate=
d by
the Food & Drug Administration (FDA), any facility changes required FDA
validation, which can itself be a costly process. This model was used to test perfo=
rmance
of product routings in the validation plan as changes to processing and
packaging resources were planned.
SUPPLY AND DEMA=
ND
MANAGEMENT
Lean programs can also benefit from simulation =
as a
tool to define policies and practices to manage production or supply with =
the
variety of demand patterns faced. <=
/span>Models
have been used within plants as described above, or across multi-location
supply chains to the ultimate consumers.&=
nbsp;
As we know, in many cases the assumptions made =
using
averages to estimate quantities or timings of operations are not adequate =
to plan
an operation. Patterns of or=
der
arrivals, variability in production processes, and other patterns can have
significant impacts on a lean design.&nbs=
p;
Many times, assumptions are also made that quantities or timings
probably follow a normal distribution, when they do not.
Following is an example of the variety of
statistical patterns that actually occur in a supply chain. The combinations of these patterns
occurring at a point in time can be the root causes of problems in an oper=
ation.
<=
![endif]>
Following is an example of a project for a
pharmaceutical company’s operation in Europe<=
/st1:place>. It summarizes our segmentation an=
alysis
methodology used, called Service Channel analysis, in combination with
simulation models to evaluate new lean management techniques across operat=
ions.
The company makes approximately 60 products, which are labeled for=
each
country, resulting in approximately 195 unique SKU’s. There is significant variability =
of
demand, which caused poor service levels in many countries.
<=
![endif]>
There was also a scheduling performance issue at the plant that
resulted in inconsistent shipments of products to re-supply the distributi=
on
network.
<=
![endif]>
The service channel analysis showed that there could be some
improvement in supply and inventory management if country labeling was
postponed.
<=
![endif]>
A simulation model was used to evaluate a series of changes in bot=
h the
plant and the supply network that included:
·  =
; Flow techniques for major high volume, low
variability products to countries and major customers,=
p>
·  =
; Labeling postponement for high cost products =
with
the establishment of a centralized labeling operation,=
p>
·  =
; Production scheduling changes for the remaini=
ng
products.
The simulation model provided detailed analyses across all SKUR=
17;s
and countries, as well as summary metrics such as follows for scenarios
evaluated.
<=
![endif]>
DEMAND PULL MODELS
Achieving a fully demand-driven process remains=
the
challenge. Many of the lean
techniques we have been using work well for high volume / low variability =
products,
but managing the mix of lower volume products, where there is significant
setup, changeover and cleanout time required have been handled outside the=
lean
program.
Simulation is ideally suited to analyze algorit=
hms,
material and information flows for both make-to-stock (MTS) and make-to-or=
der
(MTO) operations, and to decide which products should be MTS or MTO.<=
/o:p>
<=
![endif]>
<=
![endif]>
This is an example of a simulation model input/output screen used =
for
both inventory and lead time analysis for MTS, MTO and high volume flow it=
ems.
<=
![endif]>
SUMMARY
Simulation can have an important role in a lean
program, to get to the desired results more quickly, and at less cost. From its introduction over 30 yea=
rs ago,
it has allowed changes to be tried out first before introducing them to the
actual environment.
A lean program can benefit further by using it =
to
analyze many more alternatives than previously possible, and to define a
process with many variables, such as those described above, where we cannot
comprehend all the interactions occurring asynchronously in a complex
operation.
JAMES J. CUR=
RY
Jim Curry founde=
d his
business in 1986 to provide operations process improvement, IT planning and
modeling expertise. He heads
OpStat, an operations improvement company, and LogiSys, which is now a sof=
tware
company for integration middleware.
Prior to 1986 he held key information technology and operations
management positions with Emery Air Freight and Seagram.
&nbs=
p;
He has developed a lean methodology th=
at has
been implemented by large global companies that have achieved significant
operational savings and improved asset utilization. His simulation work includes mode=
ls of
complex manufacturing plants making large numbers of products, and global
supply chain models to analyze lead times and inventory levels, incorporat=
ing
supply and transit time variability.
&nbs=
p;
He has also designed =
and
developed eXconnect, a real-time integration product for supply chain
application systems on different computer systems, as a development partner
with IBM for the MQSeries.
Jim received his=
B.S.
in Mathematics from Manh=
attan
College, and M.B.A=
. in
Operations Research from Baruch College CUNY, where he received the Jerome=
Levy
Award. He is an Associ=
ate
Professor in the graduate engineering program of Fairfield
University, Fairfield, CT=
st1:place>,
teaching courses in Supply Chain Design and Lean Manufacturing using simul=
ation
modeling. He has published w=
ork for
the Journal of Business Logistics, Gartner Group, Inc., Auerbach Publisher=
s,
and the American Warehousemen's Assn.&nbs=
p;
=
Email:
JimCurry@OpStat.com  =
; Telephone
- Office: 203-43=
1-3905
Mobile 203-981-6268<=
/p>
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